This work proposes Universal Continual Adversarial Defense (UCAD), a universal framework that enables both standard and robust models to perform effective defense under the CAD setting and observes that as the number of encountered attacks increases, UCAD becomes increasingly robust, consistently enhancing the defense capability of existing robust models until saturation.
This paper advocates for a forward-thinking approach that balances technical sophistication with human-centric principles, ensuring that adversarial deep learning evolves into a discipline not just of technical defense, but also of trust, transparency, and accountability.
Maisam Abbas, Ran-Zan Wang· IEEE Open Journal of the Com...· 0 citations
Evidential Adversarial Training (EV-AT), which models uncertainty through a Dirichlet distribution and combines an evidence-based loss promoting clean accuracy and reliable uncertainty with a robust evidence-alignment loss matching clean and adversarial predictions in log Dirichlet-parameter space, is proposed.
Nicolas Sournac, Ahmed Baha Ben Jmaa, B. Braeckeveldt· 0 citations
A collaborative adversarial fine-tuning framework that maintains multiple experts during training, enables knowledge exchange through embedding-space harmonization, and consolidates the learned knowledge into a single unified robust model.
Nguyen Duc Thai, Junhao Dong, Sua Qi Rong et al.· arXiv.org· 0 citations
Adversarial training (AT) is a widely adopted defense against adversarial attacks, but its multi-step optimization process for generating adversarial examples leads to substantial computational overhead. To mitigate this, various single-step adversarial training methods have been proposed. However, these models often s...
Lifeng Huang, Yuquan Lin, Chen Wan et al.· IEEE Transactions on Informa...· 0 citations
Adversarial robustness optimization aims to preserve correct prediction under adversarial perturbations, and has produced substantial robustness gains through methods such as adversarial training and adversarial purification. However, we identify a new security risk: these gains can create shared vulnerabilities across...
Hanrui Wang, Ruihao Zheng, Shuo Wang et al.· arXiv.org· 0 citations
This paper introduces DefendMal, a novel framework that synergistically combines Denoise Autoencoder with Sequence Squeezing, a Context-aware Adversarial Generator (CAG-AdvGAN), Projected Gradient Descent (PGD) adversarial training, and a Positive–Negative Detector with Variational Autoencoder (PNDetector-VAE) to enhan...
Dennis Benedict Crasta, Vikash Kumar· Journal of Computer Virology...· 0 citations
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